Meanwhile, unsupervised methods [1]}, [2]} aim to estimate the segmentation accuracy directly from the images and labelmaps using, for example, information-theoretic and geometrical features. While unsupervised methods can be applied to scenarios where the main purpose of segmentation is to yield visually consistent results that are meaningful to a human observer, the application in medical settings is unclear.
